Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks

We propose NovoGrad, a first-order stochastic gradient method with layer-wise gradient normalization via second moment estimators and with decoupled weight decay for a better regularization. The method requires half as much memory as Adam/AdamW. We evaluated NovoGrad on the diverse set of problems, including image classification, speech recognition, neural machine translation and language modeling. On these problems, NovoGrad performed equal to or better than SGD and Adam/AdamW. Empirically we show that NovoGrad (1) is very robust during the initial training phase and does not require learning rate warm-up, (2) works well with the same learning rate policy for different problems, and (3) generally performs better than other optimizers for very large batch sizes